from util import *
import numpy as np
import pandas as pd
import string
import re
import matplotlib.pyplot as plt
%matplotlib inline
import plotly
from plotly import graph_objs
plotly.offline.init_notebook_mode()
from plotly.offline import iplot
import nltk
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from yellowbrick.text import FreqDistVisualizer
from yellowbrick.style import set_palette
set_palette('yellowbrick')
from wordcloud import WordCloud
from nltk.stem.porter import PorterStemmer
from textblob import TextBlob
from textblob import Word
from sklearn.model_selection import train_test_split
from sklearn import linear_model
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report,balanced_accuracy_score, precision_recall_curve
from sklearn.metrics import confusion_matrix
import seaborn as sn
df = pd.read_csv('labeled_data.csv')
df.head()
| Unnamed: 0 | count | hate_speech | offensive_language | neither | class | tweet | |
|---|---|---|---|---|---|---|---|
| 0 | 0 | 3 | 0 | 0 | 3 | 2 | !!! RT @mayasolovely: As a woman you shouldn't... |
| 1 | 1 | 3 | 0 | 3 | 0 | 1 | !!!!! RT @mleew17: boy dats cold...tyga dwn ba... |
| 2 | 2 | 3 | 0 | 3 | 0 | 1 | !!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby... |
| 3 | 3 | 3 | 0 | 2 | 1 | 1 | !!!!!!!!! RT @C_G_Anderson: @viva_based she lo... |
| 4 | 4 | 6 | 0 | 6 | 0 | 1 | !!!!!!!!!!!!! RT @ShenikaRoberts: The shit you... |
df['class'].value_counts()
class 1 19190 2 4163 0 1430 Name: count, dtype: int64
hate = len(df[df['class']==0])
neutral = len(df[df['class']==2])
offensive = len(df[df['class']==1])
print(f'hate : {hate} , neutral : {neutral}, offensive : {offensive}')
dist = [graph_objs.Bar(
x=['hate','offensive','neutral'],
y=[hate, offensive, neutral],
)]
plotly.offline.iplot({'data': dist, 'layout': graph_objs.Layout(title='Class Distribution Visualisation')})
hate : 1430 , neutral : 4163, offensive : 19190
"""
Remove Unnamed column from dataframe,
Keep only two classes hate_speech with class label 1 and combine offensive and neutral with class label 0
"""
df = df.drop('Unnamed: 0', axis = 1)
df['class'] = df['class'].replace(2,1)
df['class'] = df['class'].replace([0,1],[1,0])
df.head()
| count | hate_speech | offensive_language | neither | class | tweet | |
|---|---|---|---|---|---|---|
| 0 | 3 | 0 | 0 | 3 | 0 | !!! RT @mayasolovely: As a woman you shouldn't... |
| 1 | 3 | 0 | 3 | 0 | 0 | !!!!! RT @mleew17: boy dats cold...tyga dwn ba... |
| 2 | 3 | 0 | 3 | 0 | 0 | !!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby... |
| 3 | 3 | 0 | 2 | 1 | 0 | !!!!!!!!! RT @C_G_Anderson: @viva_based she lo... |
| 4 | 6 | 0 | 6 | 0 | 0 | !!!!!!!!!!!!! RT @ShenikaRoberts: The shit you... |
df['class'].value_counts()
class 0 23353 1 1430 Name: count, dtype: int64
preprocess_tweet(df, 'tweet')
df.head()
| count | hate_speech | offensive_language | neither | class | tweet | |
|---|---|---|---|---|---|---|
| 0 | 3 | 0 | 0 | 3 | 0 | as a woman you shouldnt complain about cleanin... |
| 1 | 3 | 0 | 3 | 0 | 0 | boy dats coldtyga dwn bad for cuffin dat hoe i... |
| 2 | 3 | 0 | 3 | 0 | 0 | dawg you ever fuck a bitch and she start to cr... |
| 3 | 3 | 0 | 2 | 1 | 0 | she look like a tranny |
| 4 | 6 | 0 | 6 | 0 | 0 | the shit you hear about me might be true or it... |
df1 = df.drop(columns =['count', 'hate_speech', 'offensive_language', 'neither'])
df1.head()
| class | tweet | |
|---|---|---|
| 0 | 0 | as a woman you shouldnt complain about cleanin... |
| 1 | 0 | boy dats coldtyga dwn bad for cuffin dat hoe i... |
| 2 | 0 | dawg you ever fuck a bitch and she start to cr... |
| 3 | 0 | she look like a tranny |
| 4 | 0 | the shit you hear about me might be true or it... |
df1= df1.iloc[:,[1,0]]
df1.head()
| tweet | class | |
|---|---|---|
| 0 | as a woman you shouldnt complain about cleanin... | 0 |
| 1 | boy dats coldtyga dwn bad for cuffin dat hoe i... | 0 |
| 2 | dawg you ever fuck a bitch and she start to cr... | 0 |
| 3 | she look like a tranny | 0 |
| 4 | the shit you hear about me might be true or it... | 0 |
#for ease of use, changing column name from class to target
df1 = df1.rename(columns={'class': 'target'})
X = df1.tweet
y = df1.target
X_tr, X_val, y_tr, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
train = pd.concat([X_tr, y_tr], axis=1).reset_index()
train = train.drop(columns=['index'], axis=1)
train.head()
| tweet | target | |
|---|---|---|
| 0 | well how else will white ppl get us to forget ... | 1 |
| 1 | funny thing isits not just the people doing it... | 0 |
| 2 | nigga messed with the wrong bitch | 0 |
| 3 | bitch ass nigggaaa | 0 |
| 4 | so that real bitch | 0 |
val = pd.concat([X_val, y_val], axis=1).reset_index()
val = val.drop(columns=['index'], axis=1)
zero = train[train.target == 0]
one = train[train.target == 1]
zero_tokens = tokenize(zero, 'tweet')
one_tokens = tokenize(one, 'tweet')
zero_tokenz = no_stopwords(zero_tokens)
one_tokenz = no_stopwords(one_tokens)
plot_frequency_dist(zero_tokenz, 'Non-Hate')
/Users/manveerkaur/Documents/SentimentAnalysis/util.py:133: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.
plot_frequency_dist(one_tokenz, 'Hate')
## Generate and plot wordcloud of hate tweets
plot_wordCloud(one_tokenz)
## Generate and plot wordcloud of not-hate tweets
plot_wordCloud(zero_tokenz)
hate_list = np.setdiff1d(one_tokenz, zero_tokenz)
hate_list
array(['aa', 'absolved', 'accord', 'acknowledged', 'activity', 'aflcio',
'aged', 'agg', 'ahhhahahaha', 'ahmed', 'airlines', 'aklve',
'alaqsa', 'alcoholics', 'alls', 'alsarabsss', 'amazement',
'americathey', 'amigo', 'anglo', 'anon', 'antiracist',
'antisemite', 'antizionist', 'apartheid', 'appearance',
'argentino', 'ariza', 'arkansas', 'aryan', 'aslina', 'attorney',
'axin', 'azflooding', 'azmonsoon', 'backpedals', 'baiters', 'baka',
'balless', 'ballless', 'banner', 'banwagoning', 'barge',
'barnyard', 'bateman', 'batshit', 'bazinga', 'bdubs', 'beamthat',
'beiber', 'believes', 'belton', 'benghazzi', 'benton', 'bernstine',
'beta', 'bias', 'bibles', 'bidens', 'bikes', 'birthdayyyy',
'bisexual', 'bitcheslook', 'blacklisted', 'blaspheme',
'blondeproblems', 'boris', 'boyraping', 'brainwash', 'brainwashed',
'bran', 'brits', 'bromance', 'broner', 'buckcity', 'buffets',
'buku', 'bulldozed', 'bundle', 'butcountry', 'butthole',
'buyfoodlittleguy', 'californias', 'cantstanducunt', 'capital',
'carve', 'catholics', 'caused', 'causung', 'ceasefirelets',
'celtic', 'cement', 'chava', 'chelsey', 'chimpout', 'chinatown',
'ching', 'chong', 'chood', 'chromeasome', 'chu', 'chuu', 'chyna',
'circulated', 'clans', 'clash', 'clashes', 'clones', 'clout',
'cob', 'codeword', 'combined', 'complains', 'comthablesmh',
'condone', 'conduct', 'confronts', 'connection', 'coulter',
'cousintoucher', 'coworkeri', 'cracks', 'creation',
'credibilityshot', 'criminally', 'crisco', 'crusader', 'cspan',
'dammn', 'dans', 'darling', 'dds', 'dealcry', 'dealt', 'deedee',
'deeds', 'deeeeaaaadd', 'deen', 'defence', 'delbert', 'democr',
'deviancy', 'devin', 'dicklicker', 'dickwad', 'dietoday',
'digital', 'dome', 'donts', 'doubles', 'doughnuts', 'downsize',
'drakes', 'drreams', 'dryer', 'dss', 'dtla', 'ducked', 'duis',
'dumby', 'ebloa', 'eda', 'enduring', 'engineering', 'enraged',
'entertains', 'ep', 'escape', 'establishments', 'ethiopian',
'evaaaa', 'everycunt', 'exact', 'explanation', 'faaaaggggottttt',
'facedniggers', 'faggotsfag', 'fagjo', 'fagsplease', 'fairytale',
'farmers', 'farrakhan', 'farve', 'fathom', 'faux', 'faves',
'favorited', 'feminist', 'fergusonriot', 'fieldssuburbs',
'fightpacquiao', 'fisted', 'fitz', 'flattering', 'flinched',
'flopping', 'flowing', 'foolishness', 'forced', 'forsake', 'fredo',
'fsu', 'fuckheads', 'fuckry', 'fudg', 'fuggin', 'furrybah',
'gainz', 'ganks', 'gates', 'gayer', 'gaywad', 'gaywrites',
'gazelles', 'gee', 'genetic', 'genos', 'gerryshalloweenparty',
'gettingreal', 'gezus', 'girlboy', 'glitter', 'gobbling', 'goddam',
'goddamit', 'goldbar', 'goper', 'grier', 'grilled', 'gusta',
'gypsies', 'hahahahahahaha', 'hairstyle', 'haiti', 'halfbreeds',
'hamster', 'happenings', 'happppppy', 'harassment', 'hayseed',
'healedback', 'hebrew', 'heil', 'helpful', 'hesgay', 'hesitation',
'hesters', 'heterosexual', 'heyyyyyyyyyyy', 'highlights', 'hindis',
'hires', 'hiring', 'historically', 'hitched', 'hoesand', 'hoetru',
'hollering', 'homewreckers', 'homophobic', 'honcho', 'honeybooboo',
'honour', 'hoomie', 'horrific', 'hound', 'huff', 'hugging',
'husbandry', 'hustlin', 'hypebeasts', 'ians', 'idfk', 'immoral',
'immune', 'imperfections', 'inclined', 'increase', 'indentured',
'indicator', 'indiviuals', 'infatuation', 'infest', 'infiltration',
'influenced', 'injust', 'inspect', 'intvw', 'invites', 'inviting',
'involve', 'islamnation', 'isolated', 'itwas', 'jackies', 'jai',
'japped', 'japs', 'jennas', 'jerkin', 'jigaboos', 'jock', 'judged',
'julie', 'jumpers', 'juvie', 'kakao', 'kamikaze', 'kbye',
'kennies', 'kindergarden', 'knife', 'knob', 'knockdowns',
'knooooooow', 'knowur', 'latinkings', 'leftisthomosexual',
'leftists', 'legitimizing', 'lego', 'legshis', 'lexii', 'liberty',
'lid', 'liesaboutvinscully', 'lifestyle', 'limelight', 'listeners',
'looooool', 'losangeles', 'lotto', 'lrg', 'lucas', 'lustboy',
'lynch', 'macs', 'madonnas', 'magazine', 'malt', 'manhood', 'mao',
'maoists', 'mariachi', 'maryland', 'mayoral', 'mccartney', 'medal',
'memphistn', 'merely', 'mernin', 'metlife', 'mexicannigger', 'mgr',
'mideast', 'midlaner', 'midwest', 'migrating', 'mikey',
'milesthompson', 'milwaukie', 'minorities', 'mirin', 'mischief',
'misty', 'moccasin', 'mohamed', 'molester', 'mongerls', 'mongrels',
'monkeys', 'monkies', 'moslems', 'mouthy', 'muzzy', 'naacp',
'nahhhhhaahahahaha', 'nations', 'nazis', 'nbombs', 'nebraska',
'neveraskablackperson', 'newyorkcity', 'nggas', 'nicely', 'niger',
'niggass', 'niggerous', 'nigglets', 'niggress', 'nikejordan',
'nochill', 'nonenglish', 'noneuropeans', 'nontraditional',
'nonwhites', 'notices', 'ntx', 'nurturing', 'nws', 'obese', 'odb',
'ofmine', 'okcupid', 'okiecops', 'okies', 'olympic', 'openwide',
'oppressing', 'oppressive', 'orchids', 'osamas', 'ove', 'ovenjew',
'overbreeding', 'overrun', 'oversensitive', 'panthers',
'parenthetical', 'paypay', 'peasant', 'peckin', 'pedestrian',
'peds', 'pennsylvanians', 'peoplehate', 'perish',
'pgachampionship', 'phelps', 'phillip', 'phillips', 'phrase',
'pickananny', 'pickers', 'picky', 'placement', 'placing', 'plant',
'plantation', 'polynesians', 'pontiac', 'ponytails', 'porto',
'pos', 'potheads', 'powered', 'premium', 'preparations',
'prestigious', 'priesthood', 'printer', 'printers', 'propery',
'proslavery', 'psychiatry', 'pundits', 'pussyed', 'pwi', 'queersi',
'rabchenko', 'racismisaliveandwellbro', 'radical', 'ramlogan',
'randos', 'rapists', 'rasta', 'reasonswecantbetogether',
'receptionist', 'receptionthis', 'reconnaissance', 'recruited',
'referred', 'refused', 'regionally', 'rejects', 'religions',
'repping', 'reptile', 'reside', 'restau', 'retared', 'retweeettt',
'rfn', 'rhode', 'ricans', 'roid', 'roleplayinggames', 'romeo',
'route', 'salvadoran', 'samesex', 'sandusky', 'schitt', 'scope',
'scully', 'segal', 'servant', 'sewer', 'sexist', 'shabbat',
'sharpie', 'sheboons', 'ship', 'shock', 'shoving', 'sickening',
'sidekicklike', 'sion', 'sistas', 'sixes', 'skater', 'skidmarks',
'slightlyadjusted', 'slum', 'snipe', 'soetoroobama', 'sopa',
'sophi', 'soxs', 'spaz', 'spicskkk', 'sprinkler', 'stacey',
'stalkin', 'standn', 'stds', 'stephenking', 'stereotypi',
'stoopid', 'stopsavinthesehoes', 'stu', 'stubborn', 'stuckup',
'studies', 'styl', 'styles', 'subhuman', 'subordinate', 'summers',
'suspicious', 'swaagg', 'swags', 'swill', 'sycksyllables',
'tapout', 'taxing', 'teabagged', 'teabaggerswho', 'teammate',
'teenage', 'tehgodclan', 'templars', 'terroristscongies',
'texarkana', 'thenetherlands', 'therelike', 'thetime',
'theyfaggots', 'thingsiwillteachmychild', 'thnk', 'timmys',
'tittyy', 'tmt', 'toms', 'tomyfacebro', 'toosoon', 'traditions',
'tragedy', 'trannygo', 'transformthursday', 'transmitter',
'trashiest', 'trayvonmartin', 'trout', 'tsm', 'tunis', 'tunwhat',
'tusks', 'tweetlikepontiacholmes', 'uf', 'units', 'unselfish',
'unwashed', 'uwi', 'vaca', 'vanessa', 'vddie', 'vegasshowgirls',
'vhia', 'vin', 'vinitahegwood', 'waahh', 'wacthh', 'wagging',
'wallet', 'warehouse', 'warrior', 'weapon', 'wedges', 'weirdos',
'welldid', 'wenchs', 'westvirginia', 'wher', 'whitepowerill',
'whitest', 'whomp', 'whooooo', 'whse', 'wifebeater', 'willed',
'wishywashy', 'witcho', 'woohoo', 'wooooow', 'worryol', 'wrongbut',
'wrongwitch', 'yamming', 'yaselves', 'yeawhat', 'youuuuu', 'zak',
'zigeuner', 'zion', 'zipperheads', 'zzzzzz'], dtype='<U23')
hate_tokenz = [x for x in one_tokenz if x in hate_list]
plot_frequency_dist(one_tokenz, "Hate (unique tokens)")
/Users/manveerkaur/Documents/SentimentAnalysis/util.py:133: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.
plot_wordCloud(hate_tokenz)
train.tweet = train.tweet.apply(lambda x: re.sub(r'\b\w{1,2}\b', '', str(x)))
val.tweet = val.tweet.apply(lambda x: re.sub(r'\b\w{1,2}\b', '', str(x)))
train_tokens = tokenize(train, 'tweet')
val_tokens = tokenize(val, 'tweet')
train_tokenz = no_stopwords(train_tokens)
val_tokenz = no_stopwords(val_tokens)
stop_words = set(stopwords.words('english'))
stop_list = [''.join(c for c in s if c not in string.punctuation) for s in stop_words]
train_stem = stemming(train_tokenz)
val_stem = stemming(val_tokenz)
lemmatization(train)
lemmatization(val)
train.lem = train['lem'].apply(lambda x: ' '.join([item for item in x.split() if item not in stop_list]))
val.lem = val['lem'].apply(lambda x: ' '.join([item for item in x.split() if item not in stop_list]))
train.head()
| tweet | target | lem | |
|---|---|---|---|
| 0 | well how else will white ppl get forget our ... | 1 | well else white ppl get forget horrific past p... |
| 1 | funny thing isits not just the people doing i... | 0 | funny thing isits people people seeing pic jud... |
| 2 | nigga messed with the wrong bitch | 0 | nigga messed wrong bitch |
| 3 | bitch ass nigggaaa | 0 | bitch nigggaaa |
| 4 | that real bitch | 0 | real bitch |
X_tr = train.lem
X_val = val.lem
y_tr = train.target
y_val = val.target
vec = TfidfVectorizer()
tfidf_tr = vec.fit_transform(X_tr)
tfidf_val = vec.transform(X_val)
nb = MultinomialNB().fit(tfidf_tr, y_tr)
y_pr_nb_tr = nb.predict(tfidf_tr)
y_pr_nb_val = nb.predict(tfidf_val)
get_metrics(tfidf_tr, y_tr, tfidf_val, y_val, y_pr_nb_tr, y_pr_nb_val, nb)
Training F1 Score: 0.012195121951219513 Testing F1 Score: 0.0 Training Recall Score: 0.0061403508771929825 Testing Recall Score: 0.0 Training Precision Score: 0.875 Testing Precision Score: 0.0 Training Accuracy Score: 0.9428023807121961 Testing Accuracy Score: 0.9414968731087351
/Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
make_confusion_matrix(y_val, y_pr_nb_val)
rf = RandomForestClassifier(n_estimators=100).fit(tfidf_tr, y_tr)
y_pr_rf_tr = rf.predict(tfidf_tr)
y_pr_rf_val = rf.predict(tfidf_val)
get_metrics(tfidf_tr, y_tr, tfidf_val, y_val, y_pr_rf_tr, y_pr_rf_val, rf)
Training F1 Score: 0.9824715162138475 Testing F1 Score: 0.1891117478510029 Training Recall Score: 0.9833333333333333 Testing Recall Score: 0.11379310344827587 Training Precision Score: 0.9816112084063048 Testing Precision Score: 0.559322033898305 Training Accuracy Score: 0.9979824472914355 Testing Accuracy Score: 0.942909017550938
make_confusion_matrix(y_val, y_pr_rf_val)
log = LogisticRegression().fit(tfidf_tr, y_tr)
y_pr_log_tr = log.predict(tfidf_tr)
y_pr_log_val = log.predict(tfidf_val)
get_metrics(tfidf_tr, y_tr, tfidf_val, y_val, y_pr_log_tr, y_pr_log_val, log)
Training F1 Score: 0.25584795321637427 Testing F1 Score: 0.18487394957983194 Training Recall Score: 0.15350877192982457 Testing Recall Score: 0.11379310344827587 Training Precision Score: 0.7675438596491229 Testing Precision Score: 0.4925373134328358 Training Accuracy Score: 0.9486532835670332 Testing Accuracy Score: 0.9412951381884204
make_confusion_matrix(y_val, y_pr_log_val)
df = pd.read_csv('balanced_data_combined.csv')
df.head()
| Unnamed: 0 | text | class | |
|---|---|---|---|
| 0 | 0 | Drasko they didn't cook half a bird you idiot ... | 1 |
| 1 | 1 | Hopefully someone cooks Drasko in the next ep ... | 1 |
| 2 | 2 | of course you were born in serbia...you're as ... | 1 |
| 3 | 3 | These girls are the equivalent of the irritati... | 1 |
| 4 | 4 | RT @YesYoureRacist: At least you're only a tin... | 1 |
df = df.drop(columns = 'Unnamed: 0')
df.shape
(8337, 2)
df['class'].value_counts()
class 1 4174 0 4163 Name: count, dtype: int64
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 8337 entries, 0 to 8336 Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 text 8335 non-null object 1 class 8337 non-null int64 dtypes: int64(1), object(1) memory usage: 130.4+ KB
hate = len(df[df['class']==1])
not_hate = len(df[df['class']==0])
print(f'hate : {hate} , not_hate : {not_hate}')
dist = [graph_objs.Bar(
x=['hate','not_hate'],
y=[hate, not_hate],
)]
plotly.offline.iplot({'data': dist, 'layout': graph_objs.Layout(title='Class Distribution Visualisation')})
hate : 4174 , not_hate : 4163
df.dropna(subset=['text'], inplace=True)
df = df.rename(columns={'text': 'tweet'})
preprocess_tweet(df, 'tweet')
#for ease of use, changing column name from class to target
df1 = df.rename(columns={'class': 'target'})
X = df1.tweet
y = df1.target
X_tr, X_val, y_tr, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
train = pd.concat([X_tr, y_tr], axis=1).reset_index()
train = train.drop(columns=['index'], axis=1)
train.head()
| tweet | target | |
|---|---|---|
| 0 | im not sexist but when i see a lady driving a ... | 1 |
| 1 | ur a faggot if you change your name to your bd... | 1 |
| 2 | the early bearded man gets the clam herkfacts | 0 |
| 3 | i need a meme of thatthats my day in a nutshell | 1 |
| 4 | even nature looks awesome when colored in | 0 |
val = pd.concat([X_val, y_val], axis=1).reset_index()
val = val.drop(columns=['index'], axis=1)
stop_words = set(stopwords.words('english'))
stop_list = [''.join(c for c in s if c not in string.punctuation) for s in stop_words]
train.tweet = train.tweet.apply(lambda x: re.sub(r'\b\w{1,2}\b', '', str(x)))
val.tweet = val.tweet.apply(lambda x: re.sub(r'\b\w{1,2}\b', '', str(x)))
train_tokens = tokenize(train, 'tweet')
val_tokens = tokenize(val, 'tweet')
train_tokenz = no_stopwords(train_tokens)
val_tokenz = no_stopwords(val_tokens)
train_stem = stemming(train_tokenz)
val_stem = stemming(val_tokenz)
lemmatization(train)
lemmatization(val)
train.lem = train['lem'].apply(lambda x: ' '.join([item for item in x.split() if item not in stop_list]))
val.lem = val['lem'].apply(lambda x: ' '.join([item for item in x.split() if item not in stop_list]))
zero = train[train.target == 0]
one = train[train.target == 1]
zero_tokens = tokenize(zero, 'tweet')
one_tokens = tokenize(one, 'tweet')
zero_tokenz = no_stopwords(zero_tokens)
one_tokenz = no_stopwords(one_tokens)
plot_frequency_dist(one_tokenz, 'Hate')
plot_frequency_dist(zero_tokenz, 'Non-Hate')
#word cloud of hate tokens
plot_wordCloud(one_tokenz)
#word cloud of non-hate tokens
plot_wordCloud(zero_tokenz)
hate_list = np.setdiff1d(one_tokenz, zero_tokenz)
hate_list
array(['aaaaaaaaand', 'aaand', 'aaronmacgruder', ..., 'zoes', 'zooming',
'zzzzzz'], dtype='<U29')
hate_tokenz = [x for x in one_tokenz if x in hate_list]
print(f"Total no. of unique hate tokens: {len(hate_list)}")
print(f"Count of occurence of unique hate tokens in tweets: {len(hate_tokenz)}")
Total no. of unique hate tokens: 3538 Count of occurence of unique hate tokens in tweets: 7772
plot_frequency_dist(hate_tokenz, 'Unique hate')
/Users/manveerkaur/Documents/SentimentAnalysis/util.py:133: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.
# word cloud depicting occurence of unique hate tokens
plot_wordCloud(hate_tokenz)
X_tr = train.lem
X_val = val.lem
y_tr = train.target
y_val = val.target
vec = TfidfVectorizer()
tfidf_tr = vec.fit_transform(X_tr)
tfidf_val = vec.transform(X_val)
nb = MultinomialNB().fit(tfidf_tr, y_tr)
y_pr_nb_tr = nb.predict(tfidf_tr)
y_pr_nb_val = nb.predict(tfidf_val)
get_metrics(tfidf_tr, y_tr, tfidf_val, y_val, y_pr_nb_tr, y_pr_nb_val, nb)
Training F1 Score: 0.9688663786682558 Testing F1 Score: 0.8943374197314654 Training Recall Score: 0.9742360695026963 Testing Recall Score: 0.9184652278177458 Training Precision Score: 0.9635555555555556 Testing Precision Score: 0.8714448236632537 Training Accuracy Score: 0.9686562687462508 Testing Accuracy Score: 0.8914217156568687
make_confusion_matrix(y_val, y_pr_nb_val)
rf = RandomForestClassifier(n_estimators=100).fit(tfidf_tr, y_tr)
y_pr_rf_tr = rf.predict(tfidf_tr)
y_pr_rf_val = rf.predict(tfidf_val)
get_metrics(tfidf_tr, y_tr, tfidf_val, y_val, y_pr_rf_tr, y_pr_rf_val, rf)
Training F1 Score: 0.9994010182689429 Testing F1 Score: 0.9126099706744868 Training Recall Score: 0.9997004194128221 Testing Recall Score: 0.9328537170263789 Training Precision Score: 0.9991017964071857 Testing Precision Score: 0.8932261768082663 Training Accuracy Score: 0.9994001199760048 Testing Accuracy Score: 0.910617876424715
make_confusion_matrix(y_val,y_pr_rf_val)
log = LogisticRegression().fit(tfidf_tr, y_tr)
y_pr_log_tr = log.predict(tfidf_tr)
y_pr_log_val = log.predict(tfidf_val)
get_metrics(tfidf_tr, y_tr, tfidf_val, y_val, y_pr_log_tr, y_pr_log_val, log)
Training F1 Score: 0.9637891520244461 Testing F1 Score: 0.9035087719298246 Training Recall Score: 0.944877171959257 Testing Recall Score: 0.8645083932853717 Training Precision Score: 0.9834736513875897 Testing Precision Score: 0.9461942257217848 Training Accuracy Score: 0.9644571085782844 Testing Accuracy Score: 0.907618476304739
make_confusion_matrix(y_val, y_pr_log_val)
from sklearn.model_selection import GridSearchCV
# Number of trees in random forrest
n_estimators = [int(x) for x in np.linspace(start = 50, stop = 200, num =5)]
# number of features to consider at each split
max_features = ['auto', 'sqrt']
# Max number of levels in tree
max_depth = [2,4]
# min number of samples required to splid the node
min_samples_split =[2,5]
# min number of samples required at each leaf node
min_samples_leaf =[1,2]
#The function to measure the quality of a split.
#criterion = ['gini', 'entropy', 'log_loss']
param_grid = {'n_estimators' : n_estimators,
'max_features' : max_features,
'max_depth' : max_depth,
'min_samples_split' : min_samples_split,
'min_samples_leaf' : min_samples_leaf}
#'criterion': criterion}
param_grid
{'n_estimators': [50, 87, 125, 162, 200],
'max_features': ['auto', 'sqrt'],
'max_depth': [2, 4],
'min_samples_split': [2, 5],
'min_samples_leaf': [1, 2]}
rf_momdel = RandomForestClassifier()
rf_grid = GridSearchCV(estimator = rf_momdel, param_grid = param_grid, cv = 3, verbose = 3, n_jobs = 4, scoring = 'recall' )
import warnings
warnings.filterwarnings('ignore')
rf_grid.fit(tfidf_tr, y_tr)
Fitting 3 folds for each of 80 candidates, totalling 240 fits
/Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn(
[CV 1/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.700 total time= 0.2s [CV 2/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.719 total time= 0.3s [CV 3/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.743 total time= 0.3s [CV 1/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.702 total time= 0.3s [CV 2/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.818 total time= 0.3s [CV 1/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.931 total time= 0.1s [CV 1/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.753 total time= 0.2s [CV 2/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.791 total time= 0.2s [CV 3/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.819 total time= 0.3s [CV 3/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.745 total time= 0.2s [CV 1/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.765 total time= 0.3s [CV 3/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.957 total time= 0.1s [CV 2/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.960 total time= 0.1s [CV 2/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.933 total time= 0.1s [CV 3/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.581 total time= 0.2s [CV 3/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.541 total time= 0.2s [CV 1/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.916 total time= 0.1s [CV 2/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.606 total time= 0.2s [CV 3/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.909 total time= 0.2s [CV 1/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.936 total time= 0.1s [CV 2/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.919 total time= 0.2s [CV 1/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.114 total time= 0.1s [CV 3/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.973 total time= 0.1s [CV 3/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.938 total time= 0.1s [CV 3/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.930 total time= 0.2s [CV 1/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.929 total time= 0.2s [CV 2/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.748 total time= 0.1s [CV 2/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.767 total time= 0.2s [CV 3/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.712 total time= 0.3s [CV 1/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.759 total time= 0.6s [CV 2/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.727 total time= 0.1s [CV 1/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.699 total time= 0.2s [CV 2/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.752 total time= 0.3s [CV 3/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.717 total time= 0.5s [CV 1/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.617 total time= 0.1s [CV 3/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.709 total time= 0.1s [CV 3/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.801 total time= 0.2s [CV 1/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.779 total time= 0.4s [CV 2/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.772 total time= 0.5s [CV 1/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.723 total time= 0.3s [CV 1/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.729 total time= 0.3s [CV 2/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.741 total time= 0.4s [CV 3/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.721 total time= 0.5s [CV 3/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.783 total time= 0.3s [CV 1/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.767 total time= 0.5s [CV 2/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.664 total time= 0.1s [CV 2/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.732 total time= 0.2s [CV 2/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.733 total time= 0.3s [CV 3/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.766 total time= 0.4s [CV 3/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.759 total time= 0.5s [CV 1/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.770 total time= 0.4s [CV 2/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.713 total time= 0.5s [CV 1/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.692 total time= 0.2s [CV 1/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.745 total time= 0.3s [CV 2/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.695 total time= 0.4s [CV 3/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.732 total time= 0.5s [CV 3/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.664 total time= 0.2s [CV 1/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.465 total time= 0.1s [CV 2/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.932 total time= 0.1s [CV 1/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.543 total time= 0.1s [CV 2/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.652 total time= 0.2s [CV 3/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.721 total time= 0.3s [CV 1/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.262 total time= 0.1s [CV 3/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.944 total time= 0.1s [CV 2/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.916 total time= 0.1s [CV 1/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.632 total time= 0.2s [CV 2/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.657 total time= 0.2s [CV 3/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.904 total time= 0.3s [CV 2/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.616 total time= 0.2s [CV 3/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.843 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.667 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.551 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.728 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.929 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.687 total time= 0.4s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.713 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.715 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.728 total time= 0.4s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.705 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.698 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.765 total time= 0.4s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.741 total time= 0.4s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.713 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.751 total time= 0.4s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.617 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.707 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.665 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.789 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.622 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.689 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.721 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.790 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.722 total time= 0.4s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.735 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.739 total time= 0.4s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.759 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.768 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.706 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.718 total time= 0.4s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.437 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.921 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.389 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.568 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.911 total time= 0.2s [CV 3/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.750 total time= 0.3s [CV 1/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.730 total time= 0.3s [CV 3/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.727 total time= 0.1s [CV 3/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.658 total time= 0.1s [CV 1/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.731 total time= 0.2s [CV 2/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.793 total time= 0.3s [CV 1/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.640 total time= 0.1s [CV 1/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.774 total time= 0.2s [CV 2/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.751 total time= 0.2s [CV 3/3] END criterion=entropy, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.733 total time= 0.4s [CV 1/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.450 total time= 0.2s [CV 2/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.925 total time= 0.2s [CV 3/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.520 total time= 0.1s [CV 1/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.415 total time= 0.2s [CV 2/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.941 total time= 0.2s [CV 3/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.941 total time= 0.1s [CV 1/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.876 total time= 0.2s [CV 2/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.653 total time= 0.2s [CV 1/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.346 total time= 0.1s [CV 1/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.429 total time= 0.2s [CV 2/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.907 total time= 0.2s [CV 3/3] END criterion=entropy, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.681 total time= 0.3s [CV 1/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.728 total time= 0.4s [CV 2/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.738 total time= 0.5s [CV 3/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.714 total time= 0.6s [CV 1/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.729 total time= 0.4s [CV 2/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.721 total time= 0.5s [CV 3/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.781 total time= 0.6s [CV 1/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.747 total time= 0.3s [CV 2/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.715 total time= 0.4s [CV 3/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.729 total time= 0.5s [CV 3/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.740 total time= 0.3s [CV 1/3] END criterion=entropy, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.770 total time= 0.5s [CV 2/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.694 total time= 0.1s [CV 2/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.779 total time= 0.2s [CV 2/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.743 total time= 0.3s [CV 3/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.767 total time= 0.4s [CV 1/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.713 total time= 0.1s [CV 3/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.688 total time= 0.1s [CV 3/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.674 total time= 0.2s [CV 1/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.730 total time= 0.4s [CV 2/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.757 total time= 0.5s [CV 1/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.685 total time= 0.2s [CV 1/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.742 total time= 0.3s [CV 2/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.746 total time= 0.4s [CV 3/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.751 total time= 0.5s [CV 1/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.753 total time= 0.4s [CV 2/3] END criterion=entropy, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.763 total time= 0.5s [CV 3/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.648 total time= 0.1s [CV 3/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.701 total time= 0.2s [CV 1/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.663 total time= 0.3s [CV 3/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.944 total time= 0.1s [CV 2/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.631 total time= 0.1s [CV 3/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.913 total time= 0.2s [CV 1/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.702 total time= 0.3s [CV 2/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.938 total time= 0.1s [CV 1/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.918 total time= 0.1s [CV 1/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.655 total time= 0.2s [CV 2/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.763 total time= 0.3s [CV 1/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.407 total time= 0.1s [CV 1/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.922 total time= 0.2s [CV 2/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.891 total time= 0.2s [CV 3/3] END criterion=entropy, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.663 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.777 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.696 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.702 total time= 0.4s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.718 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.709 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.713 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.696 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.747 total time= 0.4s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.730 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.606 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.728 total time= 0.4s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.694 total time= 0.4s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.758 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.696 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.468 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.953 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.683 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.763 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.703 total time= 0.4s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.958 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.942 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.720 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.731 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.590 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.513 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.709 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.778 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.742 total time= 0.4s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.352 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.933 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.898 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.974 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.531 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.562 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.906 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.961 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.952 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.606 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.595 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.269 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.902 total time= 0.1s
/Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /Users/manveerkaur/miniconda3/envs/tensorflow/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn(
GridSearchCV(cv=3, estimator=RandomForestClassifier(), n_jobs=4,
param_grid={'max_depth': [2, 4], 'max_features': ['auto', 'sqrt'],
'min_samples_leaf': [1, 2],
'min_samples_split': [2, 5],
'n_estimators': [50, 87, 125, 162, 200]},
scoring='recall', verbose=3)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. GridSearchCV(cv=3, estimator=RandomForestClassifier(), n_jobs=4,
param_grid={'max_depth': [2, 4], 'max_features': ['auto', 'sqrt'],
'min_samples_leaf': [1, 2],
'min_samples_split': [2, 5],
'n_estimators': [50, 87, 125, 162, 200]},
scoring='recall', verbose=3)RandomForestClassifier()
RandomForestClassifier()
rf_grid.best_params_
{'max_depth': 2,
'max_features': 'auto',
'min_samples_leaf': 1,
'min_samples_split': 2,
'n_estimators': 125}
rf_clf_tunned = RandomForestClassifier(n_estimators = 125, max_depth = 2, max_features = 'auto', min_samples_split=2)
rf_clf_tunned.fit(tfidf_tr, y_tr)
t_rf_test_preds_lem = rf_clf_tunned.predict(tfidf_val)
t_rf_precision = precision_score(y_val, t_rf_test_preds_lem)
t_rf_recall = recall_score(y_val, t_rf_test_preds_lem)
t_rf_acc_score = accuracy_score(y_val, t_rf_test_preds_lem)
t_rf_f1_score = f1_score(y_val, t_rf_test_preds_lem)
print('Random Forest with Hyper Parameters selected with GridSearch:')
print('Precision: {:.4}'.format(t_rf_precision))
print('Recall: {:.4}'.format(t_rf_recall))
print("Testing Accuracy: {:.4}".format(t_rf_acc_score))
print("F1 Score: {:.4}".format(t_rf_f1_score))
Random Forest with Hyper Parameters selected with GridSearch: Precision: 0.9165 Recall: 0.7638 Testing Accuracy: 0.847 F1 Score: 0.8332
fig, ax = plt.subplots(figsize=(6,6))
mat = confusion_matrix(y_val, t_rf_test_preds_lem)
sn.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,
xticklabels=['Not_Hate_Speech', 'Hate_Speech'], yticklabels=['Not_Hate_Speech', 'Hate_Speech'])
plt.xlabel('true label')
plt.ylabel('predicted label')
plt.show()
[CV 1/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.740 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.756 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.704 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.702 total time= 0.4s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.758 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.681 total time= 0.4s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.386 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.955 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.963 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.924 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.967 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.969 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.358 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.965 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.512 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.248 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.363 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.930 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.926 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.951 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.964 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.951 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.547 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.592 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.710 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.774 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.736 total time= 0.4s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.774 total time= 0.5s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.735 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.732 total time= 0.4s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.754 total time= 0.5s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.803 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.723 total time= 0.4s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.706 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.755 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.809 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.725 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.744 total time= 0.6s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.706 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.735 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.766 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.777 total time= 0.4s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.747 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.733 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.740 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.752 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.743 total time= 0.4s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.625 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.717 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.781 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.719 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.743 total time= 0.5s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.738 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.713 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.750 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.757 total time= 0.5s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.949 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.490 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.690 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.721 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.387 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.929 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.608 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.679 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.658 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.388 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.270 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.624 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.886 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.910 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.941 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.940 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.564 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.891 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.664 total time= 0.2s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.618 total time= 0.1s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.735 total time= 0.2s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.723 total time= 0.3s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.701 total time= 0.3s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.713 total time= 0.1s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.740 total time= 0.1s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.707 total time= 0.3s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.768 total time= 0.3s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.673 total time= 0.2s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.754 total time= 0.2s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.755 total time= 0.3s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.596 total time= 0.1s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.782 total time= 0.4s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.738 total time= 0.4s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.662 total time= 0.1s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.712 total time= 0.2s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.683 total time= 0.3s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.708 total time= 0.3s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.591 total time= 0.1s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.595 total time= 0.1s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.703 total time= 0.1s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.744 total time= 0.1s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.758 total time= 0.2s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.739 total time= 0.3s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.754 total time= 0.2s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.757 total time= 0.2s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.732 total time= 0.3s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.521 total time= 0.1s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.783 total time= 0.2s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.750 total time= 0.2s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.645 total time= 0.1s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.616 total time= 0.2s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.749 total time= 0.4s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.798 total time= 0.4s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.792 total time= 0.2s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.732 total time= 0.2s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.752 total time= 0.4s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.744 total time= 0.5s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.774 total time= 0.3s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.756 total time= 0.3s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.739 total time= 0.5s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.733 total time= 0.1s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.674 total time= 0.3s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.719 total time= 0.3s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.685 total time= 0.1s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.737 total time= 0.1s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.723 total time= 0.2s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.673 total time= 0.2s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.716 total time= 0.4s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.663 total time= 0.4s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.731 total time= 0.2s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.726 total time= 0.2s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.743 total time= 0.4s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.751 total time= 0.5s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.721 total time= 0.3s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.727 total time= 0.3s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.738 total time= 0.5s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.741 total time= 0.1s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.713 total time= 0.3s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.749 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.957 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.647 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.776 total time= 0.4s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.611 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.759 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.770 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.768 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.370 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.043 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.954 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.937 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.928 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.958 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.933 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.918 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.950 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.973 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.884 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.961 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.971 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.387 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.514 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.876 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.729 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.792 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.804 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.728 total time= 0.5s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.744 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.656 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.778 total time= 0.4s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.727 total time= 0.5s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.681 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.734 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.820 total time= 0.4s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.739 total time= 0.5s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.736 total time= 0.4s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.759 total time= 0.7s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.730 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.680 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.758 total time= 0.4s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.746 total time= 0.5s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.715 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.755 total time= 0.5s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.706 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.712 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.742 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.720 total time= 0.4s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.657 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.737 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.738 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.682 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.700 total time= 0.4s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.476 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.392 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.889 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.916 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.895 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.681 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.940 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.657 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.721 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.921 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.916 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.810 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.645 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.686 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.881 total time= 0.2s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.647 total time= 0.1s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.951 total time= 0.2s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.786 total time= 0.2s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.766 total time= 0.3s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.580 total time= 0.1s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.544 total time= 0.1s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.753 total time= 0.2s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.801 total time= 0.3s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.545 total time= 0.1s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.563 total time= 0.1s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.656 total time= 0.1s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.721 total time= 0.1s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.764 total time= 0.2s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.735 total time= 0.3s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.650 total time= 0.1s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.612 total time= 0.1s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.692 total time= 0.2s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.664 total time= 0.3s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.728 total time= 0.5s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.807 total time= 0.4s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.800 total time= 0.3s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.700 total time= 0.3s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.702 total time= 0.1s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.619 total time= 0.2s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.772 total time= 0.3s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.781 total time= 0.3s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.714 total time= 0.2s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.700 total time= 0.2s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.598 total time= 0.1s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.754 total time= 0.1s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.672 total time= 0.2s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.718 total time= 0.3s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.756 total time= 0.3s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.748 total time= 0.3s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.729 total time= 0.5s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.715 total time= 0.1s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.682 total time= 0.3s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.768 total time= 0.3s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.688 total time= 0.1s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.739 total time= 0.1s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.717 total time= 0.1s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.678 total time= 0.2s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.709 total time= 0.3s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.741 total time= 0.4s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.709 total time= 0.1s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.714 total time= 0.1s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.722 total time= 0.2s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.758 total time= 0.3s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.768 total time= 0.5s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.777 total time= 0.5s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.754 total time= 0.5s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.763 total time= 0.5s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.752 total time= 0.3s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.730 total time= 0.3s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.733 total time= 0.1s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.724 total time= 0.1s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.740 total time= 0.2s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.708 total time= 0.2s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.749 total time= 0.5s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.759 total time= 0.4s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.758 total time= 0.4s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.776 total time= 0.4s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.923 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.534 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.760 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.733 total time= 0.4s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.725 total time= 0.5s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.712 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.772 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.771 total time= 0.4s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.764 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.712 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.697 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.759 total time= 0.4s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.765 total time= 0.5s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.675 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.771 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.761 total time= 0.5s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.715 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.690 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.619 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.759 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.739 total time= 0.6s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.688 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.711 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.741 total time= 0.4s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.713 total time= 0.5s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.724 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.730 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.785 total time= 0.4s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.792 total time= 0.5s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.735 total time= 0.4s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.757 total time= 0.5s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.491 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.663 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.746 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.368 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.877 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.648 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.916 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.903 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.915 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.684 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.476 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.925 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.608 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.664 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.883 total time= 0.2s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.685 total time= 0.2s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.954 total time= 0.2s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.755 total time= 0.3s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.634 total time= 0.1s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.770 total time= 0.2s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.777 total time= 0.2s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.737 total time= 0.4s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.757 total time= 0.3s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.756 total time= 0.3s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.788 total time= 0.4s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.695 total time= 0.3s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.621 total time= 0.3s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.577 total time= 0.1s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.944 total time= 0.1s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.936 total time= 0.2s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.733 total time= 0.2s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.747 total time= 0.4s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.812 total time= 0.3s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.954 total time= 0.2s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.788 total time= 0.2s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.639 total time= 0.1s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.539 total time= 0.1s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.762 total time= 0.1s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.665 total time= 0.1s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.756 total time= 0.3s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.761 total time= 0.3s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.739 total time= 0.2s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.715 total time= 0.2s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.673 total time= 0.1s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.741 total time= 0.1s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.729 total time= 0.2s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.687 total time= 0.2s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.739 total time= 0.5s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.774 total time= 0.5s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.731 total time= 0.4s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.764 total time= 0.4s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.748 total time= 0.2s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.794 total time= 0.2s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.794 total time= 0.4s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.719 total time= 0.4s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.719 total time= 0.2s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.648 total time= 0.2s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.764 total time= 0.4s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.741 total time= 0.5s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.719 total time= 0.3s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.759 total time= 0.4s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.671 total time= 0.1s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.663 total time= 0.1s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.779 total time= 0.2s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.739 total time= 0.3s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.736 total time= 0.5s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.710 total time= 0.5s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.776 total time= 0.4s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.782 total time= 0.4s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.776 total time= 0.2s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.731 total time= 0.2s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.737 total time= 0.4s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.757 total time= 0.4s [CV 1/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.904 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.562 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.924 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.528 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.925 total time= 0.1s [CV 3/3] END criterion=log_loss, max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.936 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.714 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.748 total time= 0.2s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.761 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.770 total time= 0.4s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.705 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.720 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.755 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.737 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.773 total time= 0.5s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.730 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.712 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.757 total time= 0.3s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.735 total time= 0.5s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.961 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.777 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.721 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.763 total time= 0.5s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.715 total time= 0.7s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.786 total time= 0.4s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.717 total time= 0.5s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.759 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.793 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.739 total time= 0.4s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.759 total time= 0.5s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.784 total time= 0.4s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.761 total time= 0.4s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.727 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.712 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.741 total time= 0.4s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.747 total time= 0.5s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.652 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.719 total time= 0.3s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.907 total time= 0.2s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.883 total time= 0.3s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.947 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.536 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.939 total time= 0.1s [CV 2/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.630 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.710 total time= 0.2s [CV 3/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.944 total time= 0.1s [CV 1/3] END criterion=log_loss, max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.702 total time= 0.2s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.637 total time= 0.1s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.756 total time= 0.3s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.686 total time= 0.3s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.739 total time= 0.3s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.785 total time= 0.3s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.783 total time= 0.3s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.750 total time= 0.1s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.676 total time= 0.1s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.742 total time= 0.3s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.775 total time= 0.3s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.728 total time= 0.2s [CV 2/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.721 total time= 0.2s [CV 3/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.790 total time= 0.4s [CV 1/3] END max_depth=2, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.747 total time= 0.4s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.704 total time= 0.3s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.717 total time= 0.2s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.704 total time= 0.3s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.621 total time= 0.1s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.779 total time= 0.2s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.735 total time= 0.2s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.695 total time= 0.1s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.716 total time= 0.1s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.738 total time= 0.2s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.672 total time= 0.2s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.662 total time= 0.1s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.597 total time= 0.1s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.713 total time= 0.1s [CV 1/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.752 total time= 0.2s [CV 2/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.738 total time= 0.3s [CV 3/3] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.727 total time= 0.3s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.666 total time= 0.3s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=162;, score=0.762 total time= 0.4s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.680 total time= 0.1s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.708 total time= 0.1s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=87;, score=0.766 total time= 0.2s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=125;, score=0.775 total time= 0.3s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.739 total time= 0.5s [CV 3/3] END max_depth=4, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.773 total time= 0.5s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.740 total time= 0.5s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.764 total time= 0.5s [CV 1/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.779 total time= 0.4s [CV 2/3] END max_depth=4, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=162;, score=0.797 total time= 0.4s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.707 total time= 0.1s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.712 total time= 0.2s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.742 total time= 0.3s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=125;, score=0.757 total time= 0.3s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.749 total time= 0.5s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50;, score=0.752 total time= 0.1s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.740 total time= 0.4s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=162;, score=0.752 total time= 0.4s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50;, score=0.712 total time= 0.1s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=87;, score=0.692 total time= 0.2s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=125;, score=0.716 total time= 0.3s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=162;, score=0.737 total time= 0.4s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.729 total time= 0.1s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.691 total time= 0.1s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=87;, score=0.693 total time= 0.2s [CV 1/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=125;, score=0.693 total time= 0.3s [CV 2/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.750 total time= 0.4s [CV 3/3] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.770 total time= 0.4s